ggbetweenstats(
data = iris,
x = Species,
y = Sepal.Length,
title = "Distribution of sepal length across Iris species"
)
{ggstatsplot}: Informative Statistical Visualizations
Indrajeet Patil
Why {ggstatsplot}?
Current CRAN package count >23,000
{ggstatsplot} provides
📊 information-rich plots with statistical details
📝 suitable for faster (exploratory) data analysis and reporting
Informative graphic = a thousand words
Graphical summaries can reveal problems not visible from numerical statistics.
Ready-made plot = no customization
The grammar of graphics is a powerful framework (Wilkinson, 2011) and can help you make any graphics fitting your specific data visualization needs! But…
Quality of Life (QoL) improvements with {ggstatsplot}
Provide ready-made plots with defaults following the best practices in statistical reporting and data visualization.
Simpler/faster data analysis workflow
In a typical exploratory data analysis workflow, data visualization and statistical modeling are two different phases: visualization informs modeling, and modeling can suggest a different visualization, and so on and so forth.
Central idea of {ggstatsplot}
Simple: combine these two phases into one!
Other functions
Results in context of the data 🕵️
Standard approach
Pearson’s correlation test revealed that, across 142 participants, variable x was negatively correlated with variable y: \(t(140)=-0.76, p=.446\). The effect size \((r=-0.06, 95\% CI [-.23,.10])\) was small, as per Cohen’s (1988) conventions. The Bayes Factor for the same analysis revealed that the data were 5.81 times more probable under the null hypothesis as compared to the alternative hypothesis. This can be considered moderate evidence (Jeffreys, 1961) in favor of the null hypothesis (absence of any correlation between x and y).
{ggstatsplot} approach
Alternative: Pure Pain
Hunting for packages
📦 for inferential statistics ({stats})
📦 computing effect size + CIs ({effectsize})
📦 for descriptive statistics ({skimr})
📦 pairwise comparisons ({multcomp})
📦 Bayesian hypothesis testing ({BayesFactor})
📦 Bayesian estimation ({bayestestR})
📦 …
Inconsistent APIs
🤔 accepts data frame, vector, matrix?
🤔 long/wide format data?
🤔 works with NAs?
🤔 returns data frame, vector, matrix?
🤔 works with tibbles?
🤔 has all necessary details?
🤔 …
Changing aesthetics 🎨
Modification with {ggplot2} 🛠
You can modify {ggstatsplot} plots further using {ggplot2} functions. 🎉
Too much information 🙈
“Golem of Prague” issue
Promotes mindless application of statistical tests.
Easy-to-use software can lead to misuse.
Clunky API
- Too many arguments to remember.
- Not a “real”
{ggplot2}extension.
- Limited number of functions.
- Statistical proficiency needed.
Quality Assurance
Each commit must pass many QA checks:
CI Checks (GitHub Actions)
- Unit tests (random-order)
- Code coverage (100%)
- Linting (0 lints)
- Formatting (0 issues)
- Documentation (website, link rot, examples)
- CRAN checks (0 E, 0 W, 0 N)
- Pre-commit hooks (0 issues)
- Portability (Linux, macOS, Windows)
- Robustness (dependencies, R versions)
Healthy and active code base
User Love
ggwithinstats()
Hypothesis about group differences: repeated measures design
Important
✏️ Defaults
- raw data + distributions
- descriptive statistics
- inferential statistics
- effect size + uncertainty
- pairwise comparisons
- Bayesian hypothesis-testing
- Bayesian estimation
Statistical approaches available
- parametric
- parametric
- robust
- Bayesian
gghistostats()
Distribution of a numeric variable
Important
✏️ Defaults
- counts + proportion for bins
- descriptive statistics
- inferential statistics
- effect size + uncertainty
- pairwise comparisons
- Bayesian hypothesis-testing
- Bayesian estimation
Statistical approaches available
- parametric
- parametric
- robust
- Bayesian
ggdotplotstats()
Labeled numeric variable
Important
✏️ Defaults
- descriptive statistics
- inferential statistics
- effect size + uncertainty
- pairwise comparisons
- Bayesian hypothesis-testing
- Bayesian estimation
Statistical approaches available
- parametric
- parametric
- robust
- Bayesian
ggscatterstats()
Hypothesis about correlation: Two numeric variables
ggcorrmat()
Hypothesis about correlation: Multiple numeric variables
ggpiestats()
Hypothesis about composition of categorical variables
ggbarstats()
Hypothesis about composition of categorical variables
ggcoefstats()
Hypothesis about regression coefficients
Important
✏️ Defaults
- estimate + uncertainty
- inferential statistics (\(t\), \(z\), \(F\), \(\chi^2\))
- model fit indices (AIC + BIC)
Supports all regression models supported in {easystats} ecosystem.
Meta-analysis is also supported!
grouped_ functions
Best practices in statistical reporting 🏆
